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prompt-optimization

Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aiskillstore/marketplace
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  • skill-report.json15.0 KB

SKILL.md(원문)

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Prompt Optimization

This skill optimizes prompts for LLMs and AI systems, focusing on effective prompt patterns, few-shot learning, and optimal AI interactions.

When to Use This Skill

  • When building AI features or agents
  • When improving LLM response quality
  • When crafting system prompts
  • When optimizing agent performance
  • When implementing few-shot learning
  • When designing AI workflows

What This Skill Does

  1. Prompt Design: Creates effective prompts with clear structure
  2. Few-Shot Learning: Implements few-shot examples for better results
  3. Chain-of-Thought: Uses reasoning patterns for complex tasks
  4. Output Formatting: Specifies clear output formats
  5. Constraint Setting: Sets boundaries and constraints
  6. Performance Optimization: Improves prompt efficiency and results

How to Use

Optimize Prompt

Optimize this prompt for better results

Create a system prompt for a code review agent

Specific Patterns

Implement few-shot learning for this task

Prompt Techniques

Structure

Clear Sections:

  • Role definition
  • Task description
  • Constraints and boundaries
  • Output format
  • Examples

Few-Shot Learning

Pattern:

  • Provide 2-3 examples
  • Show input-output pairs
  • Demonstrate desired style
  • Include edge cases

Chain-of-Thought

Approach:

  • Break down complex tasks
  • Show reasoning steps
  • Encourage step-by-step thinking
  • Verify intermediate results

Examples

Example 1: Code Review Prompt

Input: Create optimized code review prompt

Output:

## Optimized Prompt: Code Review

### The Prompt

You are an expert code reviewer with 10+ years of experience.

Review the provided code focusing on:

  1. Security vulnerabilities
  2. Performance optimizations
  3. Code maintainability
  4. Best practices

For each issue found, provide:

  • Severity level (Critical/High/Medium/Low)
  • Specific line numbers
  • Explanation of the issue
  • Suggested fix with code example

Format your response as a structured report with clear sections.


### Techniques Used
- Role-playing for expertise
- Clear evaluation criteria
- Specific output format
- Actionable feedback requirements

Best Practices

Prompt Design

  1. Be Specific: Clear, unambiguous instructions
  2. Provide Examples: Show desired output format
  3. Set Constraints: Define boundaries clearly
  4. Iterate: Test and refine prompts
  5. Document: Keep track of effective patterns

Related Use Cases

  • AI agent development
  • LLM optimization
  • System prompt creation
  • Few-shot learning implementation
  • AI workflow design

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